Predicting Requirements for instructional Environment Design to Improve Critical Training (PREDICT)

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-09-C-0079
Agency Tracking Number: N071-099-0687
Amount: $444,633.00
Phase: Phase II
Program: SBIR
Awards Year: 2009
Solicitation Year: 2007
Solicitation Topic Code: N07-099
Solicitation Number: 2007.1
Small Business Information
12 Gill Street, Suite 1400, Woburn, MA, -
DUNS: 967259946
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Jamie Estock
 Human Factors Scientist
 (202) 842-1548
 jestock@aptima.com
Business Contact
 Margaret Clancy
Title: Human Factors Scientist
Phone: (781) 496-2415
Email: clancy@aptima.com
Research Institution
N/A
Abstract
As technology advances, the number and types of virtual training environments that training designers and acquisition professionals have to select from is exponentially growing. Reaching high levels of skill requires novel approaches to training (DoD, 2004) to ensure that training resources are allocated in a cost-effective manner that optimizes skill acquisition and retention (Ricci, Salas, & Cannon-Bowers, 1996). However, there is little guidance on how to build or acquire simulators with the appropriate fidelity to achieve specified training objectives, maintain trainee buy-in, and fit within budgetary constraints. Aptima developed the Predicting Requirements for instructional Environment Design to Improve Critical Training (PREDICT), a model-based tool to help professionals understand the effects and costs of their system fidelity options. In Phase II, Aptima will conduct focused experimentation to validate the algorithms that drive the PREDICT model and build a comprehensive cost model to ensure the most relevant costs are considered. To ensure success, the Aptima team will apply its knowledge of the fidelity literature with its expertise in predictive modeling, model-based experimentation, and experimental design.

* Information listed above is at the time of submission. *

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